Spectrophotometric Label-Free Determination of Lead Using Thiol-Functionalized Gold Nanoparticles
Bibliographic record
Abstract
A method for the determination of lead is described using thiol-functionalized gold nanoparticle. The detection method is based on the prevention of thiol-induced aggregation of gold nanoparticles by lead. Among six thiols, e.g., 4-mercapto-1-butanol, meso-2, 3-dimercaptosuccinic acid, mercaptosuccinic acid, 6-mercapto-1-hexanol, 4-(methylthio)-1-butanol, 1-propanethiol, four (4-mercapto-1-butanol, 6-mercapto-1-hexanol, 4-(methylthio)-1-butanol and 1-propanethiol) induced the aggregation of the gold nanoparticles which was measured by the change in absorbance at 520 and 650 nm. Prior incubation of the gold nanoparticles with lead decreased the 4-(methylthio)-1-butanol-induced aggregation of gold nanoparticles in a dose-dependent manner. A linear inverse relationship between the logarithmic concentration of lead and the ratio of absorbance at 650 to 520 was noted. The method has a dynamic range from 10 nM to 100 µM. However, metals such as mercury and chromium were more effective in comparison with lead in preventing the 4-methylthio-1-butanol-induced aggregation of gold nanoparticles. The method can be used for assessing the heavy metal load in water samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".